Tracking the evolution of crisis processes and mental health on social
media during the COVID-19 pandemic
- URL: http://arxiv.org/abs/2011.11024v1
- Date: Sun, 22 Nov 2020 14:30:09 GMT
- Title: Tracking the evolution of crisis processes and mental health on social
media during the COVID-19 pandemic
- Authors: Antonela Tommasel, Daniela Godoy, Juan Manuel Rodriguez
- Abstract summary: This study aims at examining the stages of crisis response and recovery as a sociological problem.
Based on a large collection of Twitter data spanning from March to August 2020 in Argentina, we present a thematic analysis on the differences in language used in social media posts.
- Score: 0.90238471756546
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The COVID-19 pandemic has affected all aspects of society, not only bringing
health hazards, but also posing challenges to public order, governments and
mental health. Moreover, it is the first one in history in which people from
around the world uses social media to massively express their thoughts and
concerns. This study aims at examining the stages of crisis response and
recovery as a sociological problem by operationalizing a well-known model of
crisis stages in terms of a psycho-linguistic analysis. Based on a large
collection of Twitter data spanning from March to August 2020 in Argentina, we
present a thematic analysis on the differences in language used in social media
posts, and look at indicators that reveal the different stages of a crisis and
the country response thereof. The analysis was combined with a study of the
temporal prevalence of mental health conversations across the time span. Beyond
the Argentinian case-study, the proposed approach and analyses can be applied
to any public large-scale data. This approach can provide insights for the
design of public health politics oriented to monitor and eventually intervene
during the different stages of a crisis, and thus improve the adverse mental
health effects on the population.
Related papers
- On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook [21.978924582262263]
Depression is the most widely studied mental health condition.
The COVID-19 global pandemic has had a great impact on mental health worldwide.
We present a survey on natural language processing (NLP) approaches to modeling depression in social media.
arXiv Detail & Related papers (2024-10-11T13:20:54Z) - Predicting Depression and Anxiety: A Multi-Layer Perceptron for
Analyzing the Mental Health Impact of COVID-19 [1.9809980686152868]
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends during the COVID-19 pandemic.
Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults.
This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health.
arXiv Detail & Related papers (2024-03-09T22:49:04Z) - Aggression and "hate speech" in communication of media users: analysis
of control capabilities [50.591267188664666]
Authors studied the possibilities of mutual influence of users in new media.
They found a high level of aggression and hate speech when discussing an urgent social problem - measures for COVID-19 fighting.
Results can be useful for developing media content in a modern digital environment.
arXiv Detail & Related papers (2022-08-25T15:53:32Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
We present CovidEmo, 1K tweets labeled with emotions.
We examine how well large pre-trained language models generalize across domains and crises.
arXiv Detail & Related papers (2021-07-23T04:07:14Z) - CovidTracker: A comprehensive Covid-related social media dataset for NLP
tasks [8.230368367333043]
This release supports the findings of a research study funded by the Scottish Government Chief Scientists' Office.
It aims to investigate social sentiment in order to understand the response to public health measures implemented during the pandemic.
arXiv Detail & Related papers (2021-03-30T15:44:48Z) - Capturing social media expressions during the COVID-19 pandemic in
Argentina and forecasting mental health and emotions [0.802904964931021]
We forecast mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on language expressions used in social media.
Mental health conditions and emotions are captured via markers, which link social media contents with lexicons.
arXiv Detail & Related papers (2021-01-12T15:15:31Z) - Social Media Unrest Prediction during the {COVID}-19 Pandemic: Neural
Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises [26.447165399064552]
We present psychologically validated social unrest predictors and replicate scalable and automated predictions.
We employ this model to investigate a change of language towards social unrest during the COVID-19 pandemic.
arXiv Detail & Related papers (2020-12-08T17:40:35Z) - Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
Country image has a profound influence on international relations and economic development.
In the worldwide outbreak of COVID-19, countries and their people display different reactions.
In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
arXiv Detail & Related papers (2020-09-12T15:54:51Z) - Understanding the temporal evolution of COVID-19 research through
machine learning and natural language processing [66.63200823918429]
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world.
We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research.
Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
arXiv Detail & Related papers (2020-07-22T18:02:39Z) - The Ivory Tower Lost: How College Students Respond Differently than the
General Public to the COVID-19 Pandemic [66.80677233314002]
Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges.
In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President.
This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
arXiv Detail & Related papers (2020-04-21T13:02:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.